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Afshin Motaghi Destenaei, Ali Karami, Milad Piri Fath Abad,
Volume 11, Issue 2 (9-2024)
Volume 11, Issue 2 (9-2024)
Abstract
Introduction
The idea of creating smart machines and artificial intelligence has been around for centuries and dates back to at least the 14th century. Although the application of artificial intelligence in education is a very new field, but during the last 25 years, artificial intelligence has made achievements in some fields. Which has also affected education of course, criticisms have also been raised against excessive optimism towards contemporary artificial intelligence research. Little research has been done on the expectations of the role of artificial intelligence in education and its potential impact on education. The purpose of this study is to analyze and investigate the role of artificial intelligence in education.
Methods and Materoal
This study was done using SWOT analysis method and its data collection method is also a library
Resultss and Discussion
Text In general, artificial intelligence as a catalyst for teaching and learning with the help of computers is a field with many applications. The teaching of science, technology, engineering and mathematics subjects can be enhanced with artificial intelligence-based software systems. Another potential strength is the potential of AI systems to serve learners across schools, borders, and platforms in creating ecosystems of interactive learning tools. Additionally, AI systems in education may be used to evaluate different learning models throughout the school. Without strong artificial intelligence, tutoring systems cannot provide rapid feedback to learners and enable stimulating interaction. With a realistic view, weak to moderate and strong artificial intelligence have a good ability to support teaching and learning and facilitate the daily work of teachers.
Intelligent learning systems often have less artificial intelligence than expected, especially when it comes to interacting with students. Baker (2016) in a critical position classified many of the existing education systems under stupid education systems. His concept for online learning is to enhance data-driven human intelligence rather than data-driven artificial intelligence. In order to more dynamically use AI in education, there is a need for training data, one of the problems that arise is how to ensure that the data is real and free from bias. As stated by Popenici and Kerr (2017), complex AI algorithms are designed by human programmers who are likely to include their own agendas or biases in the development of the system. An important aspect of high-level machine intelligence is that it customizes learning for each student, but in doing so it intervenes by standardizing content and what is expected of the student.
As reviewed by Lakin et al. (2016), it is hard to see a future where teachers are replaced by artificial intelligence systems or robots. A more positive and realistic scenario is that the role of the teacher evolves and transforms, freeing teachers from tedious daily tasks. In addition, AI in education has the potential to relieve the teacher of the burden of having all the knowledge and information that can be relevant to students. A possible use of artificial intelligence in education in the future is in the form of robots (collaborative robots) that help teachers in their daily work and tailor the learning experience to each student, for example in recording and analyzing the work of these students. And report to the teacher. The use of intelligent learning systems can provide customized instruction or instant feedback to students at any time of the day. But the depth of customization is one of the truly critical features, not superficial and personalized learning. Studies show that developers of intelligent instructional systems have been successful in their goal of adapting and surpassing computer-assisted instruction (CAI) and human teacher training in raising student test scores.
The negative change in the role of the teacher may be caused by the design of stereotypical courses with low-level multiple-choice questions and the use of teachers as content developers. Most school curricula and teacher training programs are not well prepared to take advantage of the benefits of artificial intelligence in education due to not providing artificial intelligence courses to their teachers. If teachers are not trained in the use of artificial intelligence, this can lead to misuse of the technology, for example in protecting privacy and using personal data for influence. According to Nicholas and Holmes (2018), an ethical framework should be established for the use of artificial intelligence in education, and even if adopted, it should be continuously discussed and updated to allow for the capabilities and scope of artificial intelligence and the potential use of reflect it. A growing concern among many education workers is the fear of unemployment as high-level machine intelligence systems completely take over the teaching profession. According to Popenici and Kerr (2017), artificial intelligence currently has the potential to replace a large number of teaching assistants and administrative staff in education, and therefore it is more important to investigate its impact on education. Studies show that widespread use of high-level AI systems may disrupt students' ability to learn independently and develop 21st century skills such as problem solving and critical thinking. Finally, the most severe threat to students may be AI. Surveillance cameras with built-in facial recognition. Along with machine learning, facial recognition is one area where AI is advancing much faster than AI ethics. By using this technology, schools may collect students' biometric information, for example, under the pretext of reducing the many working hours that employees spend on registration and attendance. Support using artificial intelligence systems in education and robotics is certainly an opportunity, but social robots are still in their infancy and have limited social skills. In the near future, a realistic opportunity lies in the development of robots that can provide personalized content and rapid feedback. As in the manufacturing industry, teachers will soon be able to reprogram the cobots using block programming code that doesn't require advanced programming skills. Of course, there are also threats, and for purely economic reasons, we will probably experience cases where teachers are replaced by artificial intelligence solutions in education. Universities with financial problems may be tempted to try solutions, such as Deakin University in Australia, which offers a service where any student who asks can expect tailored information and advice. However, since the common concern is how to submit assignments and how to pay for parking, such systems pose a threat to administrative staff rather than teachers. Finally, as with AI in general, ethics is a major and immediate challenge in the use of AI in education, even though the threats posed by AI in education may not be as dramatic as in other AI areas. Automatic will not be useful. Quality teaching is a complex and creative profession involving improvisation and spontaneity where humans are not easily replaced. In general evaluation, it can be said that there are many ways that artificial intelligence can help students. From identifying signs of effort to creating a more interactive and personalized learning program.
Here are four ways that artificial intelligence can have a positive impact on student learning; Personalized learning: The ability to respond to personalized learning needs is one of the most positive benefits of artificial intelligence in education. Artificial intelligence technology can easily adapt to different learning styles. AI technology can analyze students' past performance and create tailored curricula and settings based on past performance. When it comes to personalized learning, AI can also point students in the right direction for resources and other useful data and information. Artificial intelligence has the ability to provide personalized study plans for students without having to wait for interventions from learning professionals. All while meeting the overall goal of making learning easier and helping students engage with content more effectively. Ultimately, where AI really helps personalized learning is in its ability to reach students on a massive scale. With overcrowded classrooms at the elementary school level and classrooms of hundreds at the secondary level, AI can help personalize education for all students at once, making it easier for everyone to succeed. Tutoring: Sometimes students need extra help, and AI allows you to access on-demand tutoring without an in-person or live tutoring session. Because the AI uses algorithms to adapt, it can quickly change to cover the areas where students need the most support. Just like a human tutor who adapts to a student's learning style and ability to absorb information, AI tutoring systems are very useful in their ability to focus on improving and deepening student learning as a whole. The main advantage of AI-based tutoring technology is the ability to help students understand complex concepts and terms on a mass level. Finally, with artificial intelligence, access to tutoring is no longer limited to those who can afford it. In addition, instructors can spend less time helping those who do not understand the concepts. Assessment and grading: A large part of teachers' time is spent grading assignments. Artificial intelligence technology can help speed up this process. Additionally, when it comes to grading assignments, AI technology can help analyze and get feedback from students on things like grammar, content, and vocabulary. By removing this part of teachers' duties, they can focus on other aspects of teaching that are more important, such as lesson planning and student engagement. Finally, one of the biggest benefits of automated assessment is that it eliminates human error, biases, and mistakes. It can also give each student an outline of where they went wrong and how they can improve, without taking up extra time from teachers. Improving student interaction: Artificial intelligence can engage students in educational content and make learning more interesting. One of the ways that educators and teachers can incorporate artificial intelligence into the classroom is through the use of catboats. The ability of catboats to personalize and adapt to students' learning styles creates more opportunities to keep students engaged, and the fact that catboats can be accessed anytime or anywhere means that students they can work at their own pace and continue their learning outside of traditional classroom time. The fact that AI improves engagement is exciting for course planners and administrators. This means they can deliver highly personalized and interactive learning in their courses, regardless of the subject, helping to amplify the impact on people's lives. Discussed how artificial intelligence can be useful for students. In addition there is great potential impact on coaches and teachers – particularly in ways it can save time.
The three advantages of artificial intelligence in education for teachers are: 1- Predictive analysis an interesting and emerging area of artificial intelligence in education is prediction. AI can analyze data and predict which students might fall behind due to the educational gap. Predictive analytics is exciting for educators because it means students struggling with learning challenges can be identified earlier and given the tools they need to succeed. Additionally, early intervention means that students who otherwise fail or struggle might have the opportunity to become successful students by giving them the right tools to help them succeed. 2-Advanced educational methods one of the methods of using artificial intelligence in education is to improve teaching methods. Today, due to the vast amount of content and information, teachers often have little time to organize alternative learning methods without spending more than hours of classroom time. Using artificial intelligence technology, teachers have the ability to quickly put together games and simulations that help students practice and learn the lessons being taught without spending more time on lesson planning. It saves a lot of time for teachers. 3- Facilitating evaluations and grading if you ask any teacher, they will tell you that assessment is one of the most time-consuming parts of the job. One of the exciting areas of artificial intelligence in education is the use of artificial intelligence technology to improve and speed up the assessment and grading process. For example, assessments can be done in real time instead of lengthy home marking. This not only saves time for teachers, but also improves students' understanding of the material in the moment.
Conclusion
The research findings show that there are both opportunities and threats regarding the role of artificial intelligence in contemporary education. In many ways, AI appears to have a promotional mode. But like other areas of advertising, it has the potential to grow with specific applications in educational and learning activities. The results of the research show that the awareness of artificial intelligence and the study of the role of artificial intelligence in education will reduce the risk of substituting artificial intelligence instead of using artificial intelligence in education
The idea of creating smart machines and artificial intelligence has been around for centuries and dates back to at least the 14th century. Although the application of artificial intelligence in education is a very new field, but during the last 25 years, artificial intelligence has made achievements in some fields. Which has also affected education of course, criticisms have also been raised against excessive optimism towards contemporary artificial intelligence research. Little research has been done on the expectations of the role of artificial intelligence in education and its potential impact on education. The purpose of this study is to analyze and investigate the role of artificial intelligence in education.
Methods and Materoal
This study was done using SWOT analysis method and its data collection method is also a library
Resultss and Discussion
Text In general, artificial intelligence as a catalyst for teaching and learning with the help of computers is a field with many applications. The teaching of science, technology, engineering and mathematics subjects can be enhanced with artificial intelligence-based software systems. Another potential strength is the potential of AI systems to serve learners across schools, borders, and platforms in creating ecosystems of interactive learning tools. Additionally, AI systems in education may be used to evaluate different learning models throughout the school. Without strong artificial intelligence, tutoring systems cannot provide rapid feedback to learners and enable stimulating interaction. With a realistic view, weak to moderate and strong artificial intelligence have a good ability to support teaching and learning and facilitate the daily work of teachers.
Intelligent learning systems often have less artificial intelligence than expected, especially when it comes to interacting with students. Baker (2016) in a critical position classified many of the existing education systems under stupid education systems. His concept for online learning is to enhance data-driven human intelligence rather than data-driven artificial intelligence. In order to more dynamically use AI in education, there is a need for training data, one of the problems that arise is how to ensure that the data is real and free from bias. As stated by Popenici and Kerr (2017), complex AI algorithms are designed by human programmers who are likely to include their own agendas or biases in the development of the system. An important aspect of high-level machine intelligence is that it customizes learning for each student, but in doing so it intervenes by standardizing content and what is expected of the student.
As reviewed by Lakin et al. (2016), it is hard to see a future where teachers are replaced by artificial intelligence systems or robots. A more positive and realistic scenario is that the role of the teacher evolves and transforms, freeing teachers from tedious daily tasks. In addition, AI in education has the potential to relieve the teacher of the burden of having all the knowledge and information that can be relevant to students. A possible use of artificial intelligence in education in the future is in the form of robots (collaborative robots) that help teachers in their daily work and tailor the learning experience to each student, for example in recording and analyzing the work of these students. And report to the teacher. The use of intelligent learning systems can provide customized instruction or instant feedback to students at any time of the day. But the depth of customization is one of the truly critical features, not superficial and personalized learning. Studies show that developers of intelligent instructional systems have been successful in their goal of adapting and surpassing computer-assisted instruction (CAI) and human teacher training in raising student test scores.
The negative change in the role of the teacher may be caused by the design of stereotypical courses with low-level multiple-choice questions and the use of teachers as content developers. Most school curricula and teacher training programs are not well prepared to take advantage of the benefits of artificial intelligence in education due to not providing artificial intelligence courses to their teachers. If teachers are not trained in the use of artificial intelligence, this can lead to misuse of the technology, for example in protecting privacy and using personal data for influence. According to Nicholas and Holmes (2018), an ethical framework should be established for the use of artificial intelligence in education, and even if adopted, it should be continuously discussed and updated to allow for the capabilities and scope of artificial intelligence and the potential use of reflect it. A growing concern among many education workers is the fear of unemployment as high-level machine intelligence systems completely take over the teaching profession. According to Popenici and Kerr (2017), artificial intelligence currently has the potential to replace a large number of teaching assistants and administrative staff in education, and therefore it is more important to investigate its impact on education. Studies show that widespread use of high-level AI systems may disrupt students' ability to learn independently and develop 21st century skills such as problem solving and critical thinking. Finally, the most severe threat to students may be AI. Surveillance cameras with built-in facial recognition. Along with machine learning, facial recognition is one area where AI is advancing much faster than AI ethics. By using this technology, schools may collect students' biometric information, for example, under the pretext of reducing the many working hours that employees spend on registration and attendance. Support using artificial intelligence systems in education and robotics is certainly an opportunity, but social robots are still in their infancy and have limited social skills. In the near future, a realistic opportunity lies in the development of robots that can provide personalized content and rapid feedback. As in the manufacturing industry, teachers will soon be able to reprogram the cobots using block programming code that doesn't require advanced programming skills. Of course, there are also threats, and for purely economic reasons, we will probably experience cases where teachers are replaced by artificial intelligence solutions in education. Universities with financial problems may be tempted to try solutions, such as Deakin University in Australia, which offers a service where any student who asks can expect tailored information and advice. However, since the common concern is how to submit assignments and how to pay for parking, such systems pose a threat to administrative staff rather than teachers. Finally, as with AI in general, ethics is a major and immediate challenge in the use of AI in education, even though the threats posed by AI in education may not be as dramatic as in other AI areas. Automatic will not be useful. Quality teaching is a complex and creative profession involving improvisation and spontaneity where humans are not easily replaced. In general evaluation, it can be said that there are many ways that artificial intelligence can help students. From identifying signs of effort to creating a more interactive and personalized learning program.
Here are four ways that artificial intelligence can have a positive impact on student learning; Personalized learning: The ability to respond to personalized learning needs is one of the most positive benefits of artificial intelligence in education. Artificial intelligence technology can easily adapt to different learning styles. AI technology can analyze students' past performance and create tailored curricula and settings based on past performance. When it comes to personalized learning, AI can also point students in the right direction for resources and other useful data and information. Artificial intelligence has the ability to provide personalized study plans for students without having to wait for interventions from learning professionals. All while meeting the overall goal of making learning easier and helping students engage with content more effectively. Ultimately, where AI really helps personalized learning is in its ability to reach students on a massive scale. With overcrowded classrooms at the elementary school level and classrooms of hundreds at the secondary level, AI can help personalize education for all students at once, making it easier for everyone to succeed. Tutoring: Sometimes students need extra help, and AI allows you to access on-demand tutoring without an in-person or live tutoring session. Because the AI uses algorithms to adapt, it can quickly change to cover the areas where students need the most support. Just like a human tutor who adapts to a student's learning style and ability to absorb information, AI tutoring systems are very useful in their ability to focus on improving and deepening student learning as a whole. The main advantage of AI-based tutoring technology is the ability to help students understand complex concepts and terms on a mass level. Finally, with artificial intelligence, access to tutoring is no longer limited to those who can afford it. In addition, instructors can spend less time helping those who do not understand the concepts. Assessment and grading: A large part of teachers' time is spent grading assignments. Artificial intelligence technology can help speed up this process. Additionally, when it comes to grading assignments, AI technology can help analyze and get feedback from students on things like grammar, content, and vocabulary. By removing this part of teachers' duties, they can focus on other aspects of teaching that are more important, such as lesson planning and student engagement. Finally, one of the biggest benefits of automated assessment is that it eliminates human error, biases, and mistakes. It can also give each student an outline of where they went wrong and how they can improve, without taking up extra time from teachers. Improving student interaction: Artificial intelligence can engage students in educational content and make learning more interesting. One of the ways that educators and teachers can incorporate artificial intelligence into the classroom is through the use of catboats. The ability of catboats to personalize and adapt to students' learning styles creates more opportunities to keep students engaged, and the fact that catboats can be accessed anytime or anywhere means that students they can work at their own pace and continue their learning outside of traditional classroom time. The fact that AI improves engagement is exciting for course planners and administrators. This means they can deliver highly personalized and interactive learning in their courses, regardless of the subject, helping to amplify the impact on people's lives. Discussed how artificial intelligence can be useful for students. In addition there is great potential impact on coaches and teachers – particularly in ways it can save time.
The three advantages of artificial intelligence in education for teachers are: 1- Predictive analysis an interesting and emerging area of artificial intelligence in education is prediction. AI can analyze data and predict which students might fall behind due to the educational gap. Predictive analytics is exciting for educators because it means students struggling with learning challenges can be identified earlier and given the tools they need to succeed. Additionally, early intervention means that students who otherwise fail or struggle might have the opportunity to become successful students by giving them the right tools to help them succeed. 2-Advanced educational methods one of the methods of using artificial intelligence in education is to improve teaching methods. Today, due to the vast amount of content and information, teachers often have little time to organize alternative learning methods without spending more than hours of classroom time. Using artificial intelligence technology, teachers have the ability to quickly put together games and simulations that help students practice and learn the lessons being taught without spending more time on lesson planning. It saves a lot of time for teachers. 3- Facilitating evaluations and grading if you ask any teacher, they will tell you that assessment is one of the most time-consuming parts of the job. One of the exciting areas of artificial intelligence in education is the use of artificial intelligence technology to improve and speed up the assessment and grading process. For example, assessments can be done in real time instead of lengthy home marking. This not only saves time for teachers, but also improves students' understanding of the material in the moment.
Conclusion
The research findings show that there are both opportunities and threats regarding the role of artificial intelligence in contemporary education. In many ways, AI appears to have a promotional mode. But like other areas of advertising, it has the potential to grow with specific applications in educational and learning activities. The results of the research show that the awareness of artificial intelligence and the study of the role of artificial intelligence in education will reduce the risk of substituting artificial intelligence instead of using artificial intelligence in education
Mahdi Akbari Golzar, Dr Ahmad Naderi,
Volume 11, Issue 2 (9-2024)
Volume 11, Issue 2 (9-2024)
Abstract
Introduction
Blockchain technology was first introduced in 2008 as a peer-to-peer electronic payment system. This technology has since attracted widespread attention in the field of scientific research as well as industry. Blockchain has been examined from various aspects. For example, a body of research examines how blockchain's decentralized approach could completely disrupt current business models, financial systems, organizations, and civil governance. Arguably, the clearest evidence of the growth and pervasiveness of this technology is the combined blockchain market capitalization reaching more than 2.6 trillion cryptocurrencies in 2024. In addition, development activity has been steadily growing over the past decade, and numerous projects have been launched to improve the core design of the blockchain (Bitcoin) (such as Ethereum, Kava, and Solana blockchains, etc.). Several articles have systematically reviewed the studies conducted in the field of blockchain in the country using the meta-combination method, all of which focus on the review of foreign articles. Due to the growth and widespread use of blockchain technology in the country and the increase in the scope of domestic research related to it, a systematic review of the research conducted inside the country also seemed necessary. In this regard, the aim of this article is to systematically review internal articles in the blockchain field, focusing on the human-computer interaction (HCI) field of study.
Methods and Materoal
In this research, we have used the qualitative meta-method for a systematic review of blockchain research. A systematic review is a method of identifying, evaluating, and interpreting past research related to a research question, topic area, or phenomenon of interest. The focus of this review is to summarize the HCI literature on blockchain technology. We organized this literature review in four comprehensive steps, following the PRISMA systematic review protocol.
Resultss and Discussion
We found that the articles in our sample adopted one of the following two perspectives. They conducted their research either on blockchain technology (74 articles, 66%) or specifically on cryptocurrencies (37 articles, 34%). Articles related to blockchain technology mainly discuss the understanding of users' motivation, perceived risks and the application of this technology, and articles related to cryptocurrencies also deal more with the jurisprudential and legal aspects of cryptocurrencies and the analysis of transaction risks and user experience. Most empirical studies that deal with people evolve around cryptocurrency, while contributions to blockchain often lead to products or evaluations of financial and administrative systems.
After providing an overview of blockchain research in the HCI community, we present and discuss the salient themes that emerged from the literature review. We identified 4 main themes:
Blockchain technology was first introduced in 2008 as a peer-to-peer electronic payment system. This technology has since attracted widespread attention in the field of scientific research as well as industry. Blockchain has been examined from various aspects. For example, a body of research examines how blockchain's decentralized approach could completely disrupt current business models, financial systems, organizations, and civil governance. Arguably, the clearest evidence of the growth and pervasiveness of this technology is the combined blockchain market capitalization reaching more than 2.6 trillion cryptocurrencies in 2024. In addition, development activity has been steadily growing over the past decade, and numerous projects have been launched to improve the core design of the blockchain (Bitcoin) (such as Ethereum, Kava, and Solana blockchains, etc.). Several articles have systematically reviewed the studies conducted in the field of blockchain in the country using the meta-combination method, all of which focus on the review of foreign articles. Due to the growth and widespread use of blockchain technology in the country and the increase in the scope of domestic research related to it, a systematic review of the research conducted inside the country also seemed necessary. In this regard, the aim of this article is to systematically review internal articles in the blockchain field, focusing on the human-computer interaction (HCI) field of study.
Methods and Materoal
In this research, we have used the qualitative meta-method for a systematic review of blockchain research. A systematic review is a method of identifying, evaluating, and interpreting past research related to a research question, topic area, or phenomenon of interest. The focus of this review is to summarize the HCI literature on blockchain technology. We organized this literature review in four comprehensive steps, following the PRISMA systematic review protocol.
Resultss and Discussion
We found that the articles in our sample adopted one of the following two perspectives. They conducted their research either on blockchain technology (74 articles, 66%) or specifically on cryptocurrencies (37 articles, 34%). Articles related to blockchain technology mainly discuss the understanding of users' motivation, perceived risks and the application of this technology, and articles related to cryptocurrencies also deal more with the jurisprudential and legal aspects of cryptocurrencies and the analysis of transaction risks and user experience. Most empirical studies that deal with people evolve around cryptocurrency, while contributions to blockchain often lead to products or evaluations of financial and administrative systems.
After providing an overview of blockchain research in the HCI community, we present and discuss the salient themes that emerged from the literature review. We identified 4 main themes:
- Decentralized economy and smart contracts (13 articles, 12%)
- Users' understanding and participation of blockchain technology and cryptocurrencies (48 articles, 43%)
- Application of blockchain technology in a specific field (34 articles, 31%)
- Jurisprudence and legal issues around blockchain and cryptocurrencies (16 articles, 14%).
Conclusion
After completing the systematic review of domestic articles, the most interesting point for us is the difference between domestic articles and international topics. As mentioned in some parts of the article, there are three general interests in the international research space that are less observed in domestic research. The first is issues related to the concept of trust in blockchain technologies. The second is the issues related to technical infrastructure and generally the way of socio-technical interactions in society, and the third is related to blockchain-based micro-projects such as Ethereum, Kava, Solana, etc., which are not considered in Iran.
The blockchain ecosystem has experienced rapid growth over the past decade. While until recently, Ethereum was the only widely used blockchain platform supporting decentralized applications, today several new blockchains (such as Solana, Kava, Polkadot, Terra, etc.) have been launched for decentralized applications. Many believe that this new generation of blockchains, which now offer instant transactions with low transaction fees, promises the third generation of the web. Web 1.0 allowed users to read (consume) content on the Internet. Web 2.0 added authoring options and the ability to generate content, thereby enabling rich interactive Internet applications. Powered by blockchain, Web 3.0 now adds the ability to own, create, and distribute digital assets. The first signs of this paradigm shift are the emergence of decentralized finance (DeFi) and non-fungible tokens (NFT), which so far account for more than two-thirds of transactions on the Ethereum blockchain and are driving user adoption of Ethereum. These topics and developments are being noticed by researchers all over the world, but we did not find any study in these fields inside the country. This issue is particularly important from the aspect that Web 3.0 challenges human interaction and cooperation on the Internet and, in a sense, mixes the human and technological space together.The need to pay attention to these research fields as well as the acceptance of interdisciplinary studies (specifically socio-technical studies) should be taken into account in order to open a gate for understanding the fast-paced global technological developments in the space of social studies and a field for presenting theories. To provide a new society in accordance with the socio-cultural context of Iranian society.
After completing the systematic review of domestic articles, the most interesting point for us is the difference between domestic articles and international topics. As mentioned in some parts of the article, there are three general interests in the international research space that are less observed in domestic research. The first is issues related to the concept of trust in blockchain technologies. The second is the issues related to technical infrastructure and generally the way of socio-technical interactions in society, and the third is related to blockchain-based micro-projects such as Ethereum, Kava, Solana, etc., which are not considered in Iran.
The blockchain ecosystem has experienced rapid growth over the past decade. While until recently, Ethereum was the only widely used blockchain platform supporting decentralized applications, today several new blockchains (such as Solana, Kava, Polkadot, Terra, etc.) have been launched for decentralized applications. Many believe that this new generation of blockchains, which now offer instant transactions with low transaction fees, promises the third generation of the web. Web 1.0 allowed users to read (consume) content on the Internet. Web 2.0 added authoring options and the ability to generate content, thereby enabling rich interactive Internet applications. Powered by blockchain, Web 3.0 now adds the ability to own, create, and distribute digital assets. The first signs of this paradigm shift are the emergence of decentralized finance (DeFi) and non-fungible tokens (NFT), which so far account for more than two-thirds of transactions on the Ethereum blockchain and are driving user adoption of Ethereum. These topics and developments are being noticed by researchers all over the world, but we did not find any study in these fields inside the country. This issue is particularly important from the aspect that Web 3.0 challenges human interaction and cooperation on the Internet and, in a sense, mixes the human and technological space together.The need to pay attention to these research fields as well as the acceptance of interdisciplinary studies (specifically socio-technical studies) should be taken into account in order to open a gate for understanding the fast-paced global technological developments in the space of social studies and a field for presenting theories. To provide a new society in accordance with the socio-cultural context of Iranian society.
Hourieh Aarabi Moghaddam, Dr. Alireza Motameni, Dr. Ali Otarkhani,
Volume 11, Issue 2 (9-2024)
Volume 11, Issue 2 (9-2024)
Abstract
Introduction
Governance has always been a key focus throughout history across various levels of authority. The rise and expansion of the financial technology (Fintech) industry have introduced new and diverse challenges for policymakers, highlighting the growing need for an appropriate governance framework. Current global studies on Fintech governance primarily focus on the business and organizational levels, and limited research has been conducted on this topic in Iran. On a macro level, only a few studies have explored the governance of Fintech beyond the enterprise level, although it is seen as a growing field. Therefore, the need for macro-level governance in Fintech is evident both globally and in Iran. This study aims to address the question: What are the governance dimensions and components applicable to the Fintech industry? Based on this, the research seeks to develop a comprehensive framework for governance in Fintech.
Methods and Materoal
This research follows a mixed-methods approach. In the qualitative phase, key terms such as governance, Fintech, and Fintech governance were selected as the foundation for reviewing previous studies. Using meta-synthesis and content analysis, various topics related to governance and Fintech governance were collected and categorized. Data were gathered from the Scopus and Science Direct databases, and studies were filtered based on the relevance of their titles, abstracts, methodologies, and findings. A total of 28 articles were selected for meta-synthesis, and content analysis was conducted to identify governance components relevant to the Fintech industry. Some studies directly addressed governance components applicable to Fintech, while others discussed challenges within Fintech that require governance. Both aspects were incorporated into the proposed framework, leading to an initial framework of governance components for Fintech.
In the quantitative phase, the identified components were validated using the fuzzy Delphi method and potential correlations among them were explored through exploratory factor analysis (EFA). The fuzzy Delphi method was conducted using Excel with input from 15 experts, while EFA was performed using SPSS with data from 217 experts. These experts held advanced degrees in fields such as industrial management, IT management, strategic management, and public administration, with at least five years of experience in governance or Fintech management. Their insights were collected via a standardized questionnaire and analyzed accordingly. Ultimately, the final framework, comprising validated dimensions and components for Fintech governance, was presented.
Resultss
The meta-synthesis of articles on governance, Fintech, and Fintech governance identified seven components: policymaking, foresight, facilitation, regulation, infrastructure development, monitoring, and evaluation. Expert opinions on these seven governance components, as well as on Fintech and Fintech governance, were collected through a standardized fuzzy Delphi questionnaire. Standard fuzzy Delphi calculations were then applied, and the fuzzy values for each component were determined. After fuzzification, a defuzzification process was conducted to convert fuzzy values into definitive ones. The final definitive values for each component were calculated as follows: policymaking (0.82), foresight (0.71), facilitation (0.79), regulation (0.81), infrastructure development (0.71), monitoring (0.77), and evaluation (0.76). According to the fuzzy Delphi method, the acceptable definitive value for each component is 0.7, indicating that all components meet the acceptable threshold, thereby confirming all seven components.
After confirming the components, it was necessary to examine whether any latent internal correlations existed between them, allowing for their reduction into broader factors. To this end, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett's test of sphericity were applied to the components based on expert opinions. The KMO value was found to be 0.787, indicating that the components could be reduced to a number of underlying factors and that the sample size was sufficient. Additionally, Bartlett's test showed good correlations among the components within each factor.
To ensure the accuracy of the component categorization, the dimensions were first identified, and each dimension was then named according to the nature of the variables within it. The variance for each component was calculated, and the total variance explained by the extracted dimensions after rotation was determined. These values, known as eigenvalues, indicate the factors that remain in the analysis and the dimensions that can be extracted. Three factors, in total, accounted for 47.7% of the variance across all variables. These three dimensions were named regulation, strategy, and provision. According to Table 1, the components of monitoring and evaluation fell under the "regulation" dimension, the components of policymaking and foresight were grouped under the "strategy" dimension, and the components of facilitation, infrastructure development, and regulation were placed under the "provision" dimension.
Table (1). Rotated Factor Matrix
Governance has always been a key focus throughout history across various levels of authority. The rise and expansion of the financial technology (Fintech) industry have introduced new and diverse challenges for policymakers, highlighting the growing need for an appropriate governance framework. Current global studies on Fintech governance primarily focus on the business and organizational levels, and limited research has been conducted on this topic in Iran. On a macro level, only a few studies have explored the governance of Fintech beyond the enterprise level, although it is seen as a growing field. Therefore, the need for macro-level governance in Fintech is evident both globally and in Iran. This study aims to address the question: What are the governance dimensions and components applicable to the Fintech industry? Based on this, the research seeks to develop a comprehensive framework for governance in Fintech.
Methods and Materoal
This research follows a mixed-methods approach. In the qualitative phase, key terms such as governance, Fintech, and Fintech governance were selected as the foundation for reviewing previous studies. Using meta-synthesis and content analysis, various topics related to governance and Fintech governance were collected and categorized. Data were gathered from the Scopus and Science Direct databases, and studies were filtered based on the relevance of their titles, abstracts, methodologies, and findings. A total of 28 articles were selected for meta-synthesis, and content analysis was conducted to identify governance components relevant to the Fintech industry. Some studies directly addressed governance components applicable to Fintech, while others discussed challenges within Fintech that require governance. Both aspects were incorporated into the proposed framework, leading to an initial framework of governance components for Fintech.
In the quantitative phase, the identified components were validated using the fuzzy Delphi method and potential correlations among them were explored through exploratory factor analysis (EFA). The fuzzy Delphi method was conducted using Excel with input from 15 experts, while EFA was performed using SPSS with data from 217 experts. These experts held advanced degrees in fields such as industrial management, IT management, strategic management, and public administration, with at least five years of experience in governance or Fintech management. Their insights were collected via a standardized questionnaire and analyzed accordingly. Ultimately, the final framework, comprising validated dimensions and components for Fintech governance, was presented.
Resultss
The meta-synthesis of articles on governance, Fintech, and Fintech governance identified seven components: policymaking, foresight, facilitation, regulation, infrastructure development, monitoring, and evaluation. Expert opinions on these seven governance components, as well as on Fintech and Fintech governance, were collected through a standardized fuzzy Delphi questionnaire. Standard fuzzy Delphi calculations were then applied, and the fuzzy values for each component were determined. After fuzzification, a defuzzification process was conducted to convert fuzzy values into definitive ones. The final definitive values for each component were calculated as follows: policymaking (0.82), foresight (0.71), facilitation (0.79), regulation (0.81), infrastructure development (0.71), monitoring (0.77), and evaluation (0.76). According to the fuzzy Delphi method, the acceptable definitive value for each component is 0.7, indicating that all components meet the acceptable threshold, thereby confirming all seven components.
After confirming the components, it was necessary to examine whether any latent internal correlations existed between them, allowing for their reduction into broader factors. To this end, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett's test of sphericity were applied to the components based on expert opinions. The KMO value was found to be 0.787, indicating that the components could be reduced to a number of underlying factors and that the sample size was sufficient. Additionally, Bartlett's test showed good correlations among the components within each factor.
To ensure the accuracy of the component categorization, the dimensions were first identified, and each dimension was then named according to the nature of the variables within it. The variance for each component was calculated, and the total variance explained by the extracted dimensions after rotation was determined. These values, known as eigenvalues, indicate the factors that remain in the analysis and the dimensions that can be extracted. Three factors, in total, accounted for 47.7% of the variance across all variables. These three dimensions were named regulation, strategy, and provision. According to Table 1, the components of monitoring and evaluation fell under the "regulation" dimension, the components of policymaking and foresight were grouped under the "strategy" dimension, and the components of facilitation, infrastructure development, and regulation were placed under the "provision" dimension.
Table (1). Rotated Factor Matrix
| Factor (Dimension) | Component | Dimension Name | ||
| 3 | 2 | 1 | ||
| 0.631 | Monitoring | Regulation | ||
| 0.715 | Evaluation | |||
| 0.548 | Policymaking | Strategy | ||
| 0.720 | Foresight | |||
| 0.637 | Facilitation | Provision | ||
| 0.672 | Infrastructure | |||
| 0.437 | Regulation | |||
The consolidation of these dimensions and components of governance for Fintech forms the final framework that this research aims to achieve.
Conclusion and Recommendations
In governance, some tasks are fundamental, while others are specific to the needs of the Fintech industry and must be governed. The integration of these two approaches forms the proposed governance framework. Current discussions on Fintech governance mainly focus on the organizational and business levels, with limited recent research, both in Iran and globally, addressing macro-level governance for Fintech. According to Rostoy (2019), the unique challenges and issues introduced by Fintech require a new form of governance, which strengthens the foundation of this study.
By compiling and summarizing governance components and key issues for Fintech governance, seven components were identified: foresight, policymaking (Taati et al., 2021; Payandeh & Afghahi, 2023), facilitation, regulation (Sharifzadeh & Gholipour, 2003), infrastructure development (Rostoy, 2019), monitoring, and evaluation (Abrahams, 2015). After validating these components, latent correlations between them were identified, resulting in three dimensions: strategy, provision, and regulation. The strategy dimension includes foresight and policymaking, the provision dimension includes facilitation, infrastructure development, and regulation, and the regulation dimension covers monitoring and evaluation. These three dimensions form a cyclical and iterative process, with governance beginning with strategy as its foundation.
Strategic foresight and policymaking are critical to starting the governance process. Policymakers and decision-makers at the national level must implement governance through strategic planning and foresight. The consideration of macro trends and the Fintech industry’s outlook is crucial for governance under the foresight component. Policymaking involves the development of national and sectoral strategies and policies that, together with foresight, form the strategic governance process.
The provision aspect focuses on preparing the governing authorities to foster and support the growth of the Fintech industry. This includes measures such as facilitation, infrastructure development, and regulation. Facilitation, for instance, can be implemented both through soft measures (like legislation) and hard measures (such as platform and system development). The governing body, as the supreme authority, is well-positioned to oversee critical national issues like the economy and national security, thus possessing both the legal and technical power to facilitate Fintech growth. Governance is also evolving toward greater regulation, which is highly relevant and applicable to the Fintech industry.
Finally, in the last phase of the governance cycle, regulation occurs through monitoring and evaluation. To fulfill its duties towards the public good and oversee the performance of Fintech companies, the governing body must monitor the industry and evaluate its performance to ensure accountability and, if necessary, exert control and make corrections. In other words, through regulation, the Fintech industry is held accountable for its performance, and this accountability is achieved through monitoring and evaluation.
Given Iran’s political-economic structure, governance over industries, and the prevailing Islamic laws and regulations, the proposed governance framework for Fintech is applicable to Iran as well. This governance model, with a 360-degree perspective on both the specific challenges of Fintech and the general duties of governance, ensures the alignment of the Fintech industry with Iran’s macroeconomic policies. Furthermore, collaboration and synergy between the Fintech industry and the governing authorities will lead to the growth and development of the sector while ensuring the protection of public interests and citizens' rights. As such, all three pillars of governance, as outlined by Graham et al. (2003), will be balanced: the governing body fulfills its responsibilities toward society, the industry achieves its desired growth, and society benefits from the industry's advancements while safeguarding its rights.
Recommendations for the use and further development of this governance framework are as follows. First, national-level policymakers should expand the seven governance components identified in this study and apply them in accordance with their duties and responsibilities to govern the Fintech industry. Second, clarity in definitions and processes related to each component or dimension will be beneficial for both Fintech and the governing body, helping to avoid many challenges and conflicts in practice, which should be addressed by the governing body as needed. Third, while the authors have endeavored to create comprehensive dimensions and components for governance, there is room for the addition of further components and the extraction of new dimensions. Future researchers are encouraged to explore and expand upon these aspects.
Maryam Tavosi, Nader Naghshineh, Mohammad Zerehsaz, Siamak Mahboub,
Volume 11, Issue 3 (12-2024)
Volume 11, Issue 3 (12-2024)
Abstract
Philosophical inquiry into art and beauty within the Western tradition can be traced back to ancient Greece. However, the concept of aesthetic experience gained prominence in the eighteenth century (Stanford Encyclopedia of Philosophy, entry on aesthetic experience, January 20, 2023). According to the Macmillan Dictionary, the term "aesthetics" was coined in Germany during this period and did not achieve acceptance in the English language until the nineteenth century (Macmillan Dictionary). Furthermore, as noted by Boo et al. (2018), the term is derived from the Latin phrase "aisthitiki," which translates to "perception through sensation." The Merriam-Webster Dictionary defines aesthetics as "pleasing appearance." The fundamental meaning of beauty is encapsulated in the notion of "maintaining unity amidst diversity" (Moshagen & Tilsch, 2010, as cited in Venture, 1876).
While beauty is a widely discussed concept in the field of art, it assumes a different significance within human-computer interaction, where it is referred to as "computational aesthetics." In 1994, Jakob Nielsen proposed a set of ten influential factors designed to enhance user interaction systems. Among these factors is the principle of "aesthetic and minimalist design," which highlights the importance of reducing clutter in user interfaces. Understanding the dimensions of aesthetics can assist web designers in creating improved user interfaces. The current research aims to identify, rank, and propose a conceptual framework for the aesthetic components of digital images on the web. The rapid expansion of web-based technologies has led to an increasing volume of data and information production. Concurrently, the understanding of aesthetics—previously discussed in non-web or offline contexts—has now emerged in online environments utilizing digital tools. Moreover, cognitive sciences have gained particular significance in contemporary research priorities. According to Wong and Borman (2014), websites must not only be usable but also visually appealing. Despite extensive research conducted in usability, psychological aspects related to aesthetics within web environments have received considerably less attention (Wong & Borman, 2014). This study aims to address this gap by focusing on identifying the characteristics of images in web environments from an aesthetic perspective.
Methods and Materials
The present research was conducted using a meta-synthesis method. Documents were retrieved from six databases: IRANDOC, ISC, SID, Google Scholar, Emerald, and Web of Science, utilizing a targeted keyword search and systematic approach that included 1,278 documents. Out of these, 54 documents were selected for inclusion in the study following the PRISMA approach. The importance coefficient of the identified codes was calculated using Shannon's qualitative content analysis method. EndNote software was employed for careful document storage and review. Initially, a foundational conceptual framework comprising 22 aesthetic characteristics for web images was developed based on insights from scholars and established sources. Subsequently, through meta-analysis, this framework was expanded to include 32 aesthetic codes applicable to images in web environments.
Results and Discussion
The basic conceptual framework was developed based on aesthetic theories from Kant, Berlyne, Leibniz, Adorno, Birkhoff, and Husserl, incorporating insights from 15 English-language documents that contained two categories, four concepts, and 22 aesthetic codes. Through meta-synthesis, this framework was enhanced to include two categories, four concepts, and 32 codes. In order of priority, the codes "symmetry or proportion" and "lack of complexity" exhibited the highest Shannon importance coefficient within the category of objective aesthetics and classical aesthetic concepts. Additionally, the codes "appealing color combination" and "moderate complexity—not too low and not too high (similar to Berlyne's theory of stimulus complexity)" were identified as having significant relevance within subjective aesthetics and classical notions of beauty. The category of subjective aesthetics pertains to users' perceptions as subjects interpreting images within web environments; conversely, objective aesthetics relates to the design of uploaded images themselves as objects within this interaction. Classical aesthetic concepts address elements that are independent of meaning and appearance; in contrast, semantic aesthetics focuses on aspects related to meaning and associations rather than mere appearances.
Conclusion
It is essential to consider both subjective and objective aesthetic codes equally. This research underscores the importance of scientific collaboration between experts in computer science and humanities to enhance understanding of aesthetics and improve human-computer interactions. The proposed conceptual framework represents a pioneering effort at both national (Iran) and international levels. It is recommended that developers of the Python library "Athec" utilize this conceptual framework to more accurately define the aesthetic characteristics of digital images within web environments by incorporating a broader range of aesthetic codes into their library programming.
While beauty is a widely discussed concept in the field of art, it assumes a different significance within human-computer interaction, where it is referred to as "computational aesthetics." In 1994, Jakob Nielsen proposed a set of ten influential factors designed to enhance user interaction systems. Among these factors is the principle of "aesthetic and minimalist design," which highlights the importance of reducing clutter in user interfaces. Understanding the dimensions of aesthetics can assist web designers in creating improved user interfaces. The current research aims to identify, rank, and propose a conceptual framework for the aesthetic components of digital images on the web. The rapid expansion of web-based technologies has led to an increasing volume of data and information production. Concurrently, the understanding of aesthetics—previously discussed in non-web or offline contexts—has now emerged in online environments utilizing digital tools. Moreover, cognitive sciences have gained particular significance in contemporary research priorities. According to Wong and Borman (2014), websites must not only be usable but also visually appealing. Despite extensive research conducted in usability, psychological aspects related to aesthetics within web environments have received considerably less attention (Wong & Borman, 2014). This study aims to address this gap by focusing on identifying the characteristics of images in web environments from an aesthetic perspective.
Methods and Materials
The present research was conducted using a meta-synthesis method. Documents were retrieved from six databases: IRANDOC, ISC, SID, Google Scholar, Emerald, and Web of Science, utilizing a targeted keyword search and systematic approach that included 1,278 documents. Out of these, 54 documents were selected for inclusion in the study following the PRISMA approach. The importance coefficient of the identified codes was calculated using Shannon's qualitative content analysis method. EndNote software was employed for careful document storage and review. Initially, a foundational conceptual framework comprising 22 aesthetic characteristics for web images was developed based on insights from scholars and established sources. Subsequently, through meta-analysis, this framework was expanded to include 32 aesthetic codes applicable to images in web environments.
Results and Discussion
The basic conceptual framework was developed based on aesthetic theories from Kant, Berlyne, Leibniz, Adorno, Birkhoff, and Husserl, incorporating insights from 15 English-language documents that contained two categories, four concepts, and 22 aesthetic codes. Through meta-synthesis, this framework was enhanced to include two categories, four concepts, and 32 codes. In order of priority, the codes "symmetry or proportion" and "lack of complexity" exhibited the highest Shannon importance coefficient within the category of objective aesthetics and classical aesthetic concepts. Additionally, the codes "appealing color combination" and "moderate complexity—not too low and not too high (similar to Berlyne's theory of stimulus complexity)" were identified as having significant relevance within subjective aesthetics and classical notions of beauty. The category of subjective aesthetics pertains to users' perceptions as subjects interpreting images within web environments; conversely, objective aesthetics relates to the design of uploaded images themselves as objects within this interaction. Classical aesthetic concepts address elements that are independent of meaning and appearance; in contrast, semantic aesthetics focuses on aspects related to meaning and associations rather than mere appearances.
Conclusion
It is essential to consider both subjective and objective aesthetic codes equally. This research underscores the importance of scientific collaboration between experts in computer science and humanities to enhance understanding of aesthetics and improve human-computer interactions. The proposed conceptual framework represents a pioneering effort at both national (Iran) and international levels. It is recommended that developers of the Python library "Athec" utilize this conceptual framework to more accurately define the aesthetic characteristics of digital images within web environments by incorporating a broader range of aesthetic codes into their library programming.
Niusha Bagheri, Margan Kian, Masoud Gramipour, Vaghar Ali Ali Azimi, Youssef Mahdavi Nesab,
Volume 11, Issue 3 (12-2024)
Volume 11, Issue 3 (12-2024)
Abstract
Purpose: Institutions such as virtual classes, schools, and universities are essential tools for enhancing academic skills. This study aims to investigate the effectiveness of Kharazmi University's e-learning program by applying the HELAM conceptual model as a framework for evaluation.
Research method: This study employed a survey research design. The target population consisted of graduate students at Kharazmi University, from which a random sample of 536 postgraduate students was selected using stratified random sampling method. A researcher-made questionnaire, based on the HELAM model and supplemented with an "overall satisfaction" factor, was used to collect data. The questionnaire was refined and translated using specialized texts and relevant research literature. Data analysis was conducted using various statistical tests, including one-sample t test and one-way analysis of variance (ANOVA) in SPSS software, as well as confirmatory factor analysis in R software.
Findings: The findings revealed that Kharazmi University's e-learning program, as evaluated using the HELAM conceptual model, exceeded the societal average in all seven dimensions with a high degree of confidence (>99%). Notably, the support issues dimension stood out as significantly different from the others. The dimensions of content quality and service quality were found to be closely related, yet distinct from the other subscales. Finally, the dimensions of system quality, professors' attitude, overall satisfaction, and student attitude had the lowest average rankings
Conclusion: To further enhance the e-learning program, the managers and experts at Kharazmi University's Information and Communication Technology Center should focus on improving the system quality, professors' attitude, overall satisfaction, and student attitude dimensions. By doing so, they can elevate the performance of these areas to match the already strong support issues dimension, ultimately leading to a more comprehensive and effective e-learning experience.
Dr. Afshin Hamdipour, Dr. Hashem Atapour, Negin Kajaiee,
Volume 11, Issue 3 (12-2024)
Volume 11, Issue 3 (12-2024)
Abstract
Information Seeking Behavior is a broad term encompassing a series of actions undertaken to articulate individuals’ information needs, search for information, evaluate it and select relevant data, ultimately leading to its use (Ozowa and Aba, 2017). According to Case and Given (2016), information-seeking is an integral part of human life. They note that humans frequently feel the need for information and actively seek it throughout their daily lives. In their research, which examined the information-seeking behaviors of professionals from various fields, including physicians, nurses, managers, engineers, journalists, customers, and other groups, the authors found significant differences in the information-seeking behaviors of various professions. These differences can be attributed to professional roles, work environments, and specific information needs. As a dimension of human behavior, information-seeking is influenced by numerous factors. Given the critical role of psychological aspects in shaping human information-seeking behavior and their impact on the interaction between humans and information, addressing these factors is vital. The increasing focus on user-centered (human-centered) studies in recent decades highlights the importance of such studies. This research explores personality traits that influence the information-seeking behavior of graduate students at the University of Tabriz.
Methods and Materials
This study used a descriptive-survey method. The statistical population comprised 2,826 graduate students (2,258 master’s and 568 doctoral students from 17 faculties at the University of Tabriz, excluding dependent units and the international campus, during the first semester of the 2022-2023 academic year. The students were enrolled in four fields: humanities, basic sciences, engineering, and agriculture. Using Cochran’s formula, the sample size was calculated to be 338 students selected through stratified random sampling. The study employed a localized version of John and Srivastava’s (1999) questionnaire for data collection. The questionnaire included two sections: six demographic items and 42 items rated on a five-point Likert scale to assess information-seeking behavior and five personality traits (Extraversion, Conscientiousness, Agreeableness, Openness to Experience, Neuroticism). Validity was ensured through expert review by five faculty members, and reliability was confirmed using Cronbach’s alpha, with coefficients ranging from 0.588 to 0.903. Data were analyzed using descriptive statistics (frequency, mean, standard deviation) and inferential statistics (multiple linear regression). Skewness and kurtosis coefficients that fell within ±2 confirmed the normal distribution of the data.
Results and Discussion
The findings of the present study showed that all five dimensions of personality traits (extroversion, conscientiousness, adaptability, acceptance of experience, and neuroticism with averages of 4.13, 3.94, 3.99, 4.11, and 2.69 respectively) have a significant effect on the information-seeking behavior of graduate students at Tabriz University; Specifically, Extraversion, Conscientiousness, Agreeableness, and Openness to Experience demonstrated positive effects, while Neuroticism exhibited a negative effect. Other results showed that among information-seeking behaviors, "referring to the Internet to obtain information" has the highest priority among students, with an average of 4.72. In the extraversion dimension, "being friendly in the process of acquiring information" is the most important, with an average of 4.34. In the dimension of conscientiousness, "observance of order in the process of obtaining information" has the highest average score, with an average score of 4.22. In the adaptability dimension, "tendency to cooperate with others during information searching" has the highest score with an average of 4.29. In the experience acceptance dimension, "having an active imagination in the information seeking process" has the highest rank with an average of 4.42. In the dimension of neuroticism, "being nervous in the process of finding information" is the highest average score (3.03). The results of multiple linear regression also showed that the independent variables, extroversion, conscientiousness, adaptability, acceptance of experience, and neuroticism are significant predictors of information-seeking behavior, which explain 25.6% of the changes related to the dependent variable. Among the 5 independent variables, the conscientiousness variable, with a beta coefficient of 0.220, made a greater contribution than the other variables.
Conclusion
The findings of this study confirmed the effect of five important personality traits on information-seeking behavior. It is expected that librarians and information specialists will consider the different aspects of personality traits in information-seeking behavior and pay attention to the fact that knowledge of these issues will help them to provide effective information services to students. According to the findings of the present study, it is recommended that the libraries of University of Tabriz establish information systems based on individual student differences to facilitate an optimal environment for information searching. In addition, organizing workshops on communication skills can help students perform more effectively in information-seeking activities. These skills can be beneficial for both extroverted and even neurotic students. It is also recommended that information system designers tailor their systems and services based on the needs and personality traits of students. Furthermore, it is recommended that librarians receive the necessary training to identify students’ individual characteristics and differences and provide information services tailored to their personality traits during interactions with users. Finally, offering psychological counseling and stress management support for students can help them reduce their anxiety and improve their performance in information-seeking activities. This is particularly beneficial for students with high neuroticism levels.
Methods and Materials
This study used a descriptive-survey method. The statistical population comprised 2,826 graduate students (2,258 master’s and 568 doctoral students from 17 faculties at the University of Tabriz, excluding dependent units and the international campus, during the first semester of the 2022-2023 academic year. The students were enrolled in four fields: humanities, basic sciences, engineering, and agriculture. Using Cochran’s formula, the sample size was calculated to be 338 students selected through stratified random sampling. The study employed a localized version of John and Srivastava’s (1999) questionnaire for data collection. The questionnaire included two sections: six demographic items and 42 items rated on a five-point Likert scale to assess information-seeking behavior and five personality traits (Extraversion, Conscientiousness, Agreeableness, Openness to Experience, Neuroticism). Validity was ensured through expert review by five faculty members, and reliability was confirmed using Cronbach’s alpha, with coefficients ranging from 0.588 to 0.903. Data were analyzed using descriptive statistics (frequency, mean, standard deviation) and inferential statistics (multiple linear regression). Skewness and kurtosis coefficients that fell within ±2 confirmed the normal distribution of the data.
Results and Discussion
The findings of the present study showed that all five dimensions of personality traits (extroversion, conscientiousness, adaptability, acceptance of experience, and neuroticism with averages of 4.13, 3.94, 3.99, 4.11, and 2.69 respectively) have a significant effect on the information-seeking behavior of graduate students at Tabriz University; Specifically, Extraversion, Conscientiousness, Agreeableness, and Openness to Experience demonstrated positive effects, while Neuroticism exhibited a negative effect. Other results showed that among information-seeking behaviors, "referring to the Internet to obtain information" has the highest priority among students, with an average of 4.72. In the extraversion dimension, "being friendly in the process of acquiring information" is the most important, with an average of 4.34. In the dimension of conscientiousness, "observance of order in the process of obtaining information" has the highest average score, with an average score of 4.22. In the adaptability dimension, "tendency to cooperate with others during information searching" has the highest score with an average of 4.29. In the experience acceptance dimension, "having an active imagination in the information seeking process" has the highest rank with an average of 4.42. In the dimension of neuroticism, "being nervous in the process of finding information" is the highest average score (3.03). The results of multiple linear regression also showed that the independent variables, extroversion, conscientiousness, adaptability, acceptance of experience, and neuroticism are significant predictors of information-seeking behavior, which explain 25.6% of the changes related to the dependent variable. Among the 5 independent variables, the conscientiousness variable, with a beta coefficient of 0.220, made a greater contribution than the other variables.
Conclusion
The findings of this study confirmed the effect of five important personality traits on information-seeking behavior. It is expected that librarians and information specialists will consider the different aspects of personality traits in information-seeking behavior and pay attention to the fact that knowledge of these issues will help them to provide effective information services to students. According to the findings of the present study, it is recommended that the libraries of University of Tabriz establish information systems based on individual student differences to facilitate an optimal environment for information searching. In addition, organizing workshops on communication skills can help students perform more effectively in information-seeking activities. These skills can be beneficial for both extroverted and even neurotic students. It is also recommended that information system designers tailor their systems and services based on the needs and personality traits of students. Furthermore, it is recommended that librarians receive the necessary training to identify students’ individual characteristics and differences and provide information services tailored to their personality traits during interactions with users. Finally, offering psychological counseling and stress management support for students can help them reduce their anxiety and improve their performance in information-seeking activities. This is particularly beneficial for students with high neuroticism levels.
Farhad Fathi, Kourosh Fathi Vagargah, Esmaeil Jafari, Mojtaba Vahidi Asl,
Volume 11, Issue 3 (12-2024)
Volume 11, Issue 3 (12-2024)
Abstract
Businesses affected by digital transformations are facing new employee management and development needs. Employees in these companies not only need to acquire the right technical skills, but also have the mindset to help them cope with the new challenges of the digital workforce in the modern world. These changes and needs that are subsequently created in the development path lead to a digital transformation in the training of managers, as trainers and training professionals need to transition to new work forms to find, create and use digital tools to help future managers, companies and employees. The evolving literature of electronic human resource management expresses its challenges and potential. Stone et al. (2015) found that data-driven decision-making environments in the field of human interactions have a high ability to evaluate recruitment volunteers, improve staff levels, as well as provide digital tools for employee training and development. However, most studies in electronic Human Resource Management have concluded that more innovation is needed to improve the efficiency and performance of these digital tools.
In 2010, ifenthaler stated that in the not-too-distant future, when learners become active builders of their learning environments, setting individual goals and creating content structures for the knowledge and content they want to master, we may see the emergence of the true meaning of Constructivism (Ifenthaler, 2010) and that is now when eifenthaler mentioned it 12 years ago, and on this basis, the fundamental issue of research can be seen as the mismatch of the current situation.education and human resource development with new technologies. The digital age requires digital transformation in the most important context of humanity, the platform of teaching and learning. On the other hand, although the severity of the covid-19 pandemic has decreased and training has been resumed from the virtual platform, in the digital world and the volume of available data and the moment-to-moment updating of information, it is never possible to transfer them through face-to-face training. On the other hand, a person does not have the capacity to learn all the information and data available, so it is desirable that what he learns is based on his personal development, interests and expertise to make learning deeper and more effective. So this research seeks to address or adjust these issues to take a step towards improving the education and Human Resource Development situation in the country, and this will be achieved by designing a model of AI-based digital curriculum. To this end, the current research questions include:
1. What are the components of AI from the point of view of commentators?
2. What is the concept of digital curriculum from the point of view of commentators?
3. What are the coordinates of the AI-based digital curriculum model?
Methods and Materoal
Based on the purpose, the present research is applied, and in terms of data collection, it is a qualitative design. Among the various qualitative methods, the grounded theory method of the foundation was used with the constructivist approach of Charmaz. The current research community is all specialists in the field of curriculum, educational technology, educational technology and artificial intelligence, and the samples included 23 specialists. In order to collect information, semi-structured interview, observation and study of documents were used. In order to analyze the data in this research, the three-step method of Susanne Friese including noticing, collecting and thinking was done with the help of Atlas t.i software.
Resultss and Discussion
In 2010, ifenthaler stated that in the not-too-distant future, when learners become active builders of their learning environments, setting individual goals and creating content structures for the knowledge and content they want to master, we may see the emergence of the true meaning of Constructivism (Ifenthaler, 2010) and that is now when eifenthaler mentioned it 12 years ago, and on this basis, the fundamental issue of research can be seen as the mismatch of the current situation.education and human resource development with new technologies. The digital age requires digital transformation in the most important context of humanity, the platform of teaching and learning. On the other hand, although the severity of the covid-19 pandemic has decreased and training has been resumed from the virtual platform, in the digital world and the volume of available data and the moment-to-moment updating of information, it is never possible to transfer them through face-to-face training. On the other hand, a person does not have the capacity to learn all the information and data available, so it is desirable that what he learns is based on his personal development, interests and expertise to make learning deeper and more effective. So this research seeks to address or adjust these issues to take a step towards improving the education and Human Resource Development situation in the country, and this will be achieved by designing a model of AI-based digital curriculum. To this end, the current research questions include:
1. What are the components of AI from the point of view of commentators?
2. What is the concept of digital curriculum from the point of view of commentators?
3. What are the coordinates of the AI-based digital curriculum model?
Methods and Materoal
Based on the purpose, the present research is applied, and in terms of data collection, it is a qualitative design. Among the various qualitative methods, the grounded theory method of the foundation was used with the constructivist approach of Charmaz. The current research community is all specialists in the field of curriculum, educational technology, educational technology and artificial intelligence, and the samples included 23 specialists. In order to collect information, semi-structured interview, observation and study of documents were used. In order to analyze the data in this research, the three-step method of Susanne Friese including noticing, collecting and thinking was done with the help of Atlas t.i software.
Resultss and Discussion
- What are the components of AI from the point of view of commentators?
The components of artificial intelligence consisted of 5 Main and 19 sub-categories. These include charting systems (algorithm, phase logic, classification), learning systems (supervised learning ,unsupervised learning, hybrid knowledge - based systems, reinforcement learning, learning from incomplete data), semantic systems (self-learning, semantic similarity, natural language understanding, prediction), control of complex systems (dealing with nonlinear problems, expert system), neural network model (problem solving, optimization, flexibility, reasoning).
2. What is the concept of digital curriculum from the point of view of commentators?
The concept of digital curriculum has 6 Main and 33 sub-categories. These categories include digital curriculum objectives (increasing the capacity of program design by teachers, developing cognitive skills, meaningful learning experiences, participatory learning opportunities, educational dynamics, research-oriented, educational justice, self-learning), digital curriculum features (stable yet flexible, transforming learning into a lifelong process, balancing the learner and learning environment, using technology in the classroom, digital teaching culture, high compliance capacity), digital curriculum tools (educational games, digital laboratories, electronic libraries, simulators, environmental features of the digital curriculum (interactive, flexible, classroom Networking lessons, personalization of the learning environment), digital curriculum resources (Smart Textbooks,personalization of learning resources, web-based resources, open educational resources, textbooks), evaluation methods in the digital curriculum (online tests, video dialogue, video recorded by the learner, online critical texts, digital evaluation tools, quizzes).
3. What are the coordinates of the AI-based digital curriculum model?
phase curriculum model includes phase1 curriculum (learning based on specific pattern, classification and organization of content, linear learning, learning under external supervision, reinforcement learning and mutual understanding of language), phase2 curriculum (combined knowledge in learning, optimal building learning, learning from incomplete data, reasoning-based learning, predicting the learning process and facing learning problems) and phase3 curriculum (facing non-linear problems, deep learning, unsupervised learning, expertise in learning, semantic parallelism, self-directed learning and flexibility in learning).
Conclusion
Digital transformations have significantly changed teaching and learning practices. The present study examines the new needs of employee development and empowerment in the digital age, identifying the components of artificial intelligence and digital curriculum. The main objective of the present study is to define the components of artificial intelligence and then apply them in the form of digital curriculum elements. In other words, the digital curriculum in the workplace is defined by the components and functions of artificial intelligence.This model is designed based on the phase logic of artificial intelligence and can help to improve the design of the digital workplace curriculum. Based on the background studies, no research was found that could organize the digital workplace curriculum in this way, and therefore, the findings of the current research and the final output were completely unique.
2. What is the concept of digital curriculum from the point of view of commentators?
The concept of digital curriculum has 6 Main and 33 sub-categories. These categories include digital curriculum objectives (increasing the capacity of program design by teachers, developing cognitive skills, meaningful learning experiences, participatory learning opportunities, educational dynamics, research-oriented, educational justice, self-learning), digital curriculum features (stable yet flexible, transforming learning into a lifelong process, balancing the learner and learning environment, using technology in the classroom, digital teaching culture, high compliance capacity), digital curriculum tools (educational games, digital laboratories, electronic libraries, simulators, environmental features of the digital curriculum (interactive, flexible, classroom Networking lessons, personalization of the learning environment), digital curriculum resources (Smart Textbooks,personalization of learning resources, web-based resources, open educational resources, textbooks), evaluation methods in the digital curriculum (online tests, video dialogue, video recorded by the learner, online critical texts, digital evaluation tools, quizzes).
3. What are the coordinates of the AI-based digital curriculum model?
phase curriculum model includes phase1 curriculum (learning based on specific pattern, classification and organization of content, linear learning, learning under external supervision, reinforcement learning and mutual understanding of language), phase2 curriculum (combined knowledge in learning, optimal building learning, learning from incomplete data, reasoning-based learning, predicting the learning process and facing learning problems) and phase3 curriculum (facing non-linear problems, deep learning, unsupervised learning, expertise in learning, semantic parallelism, self-directed learning and flexibility in learning).
Conclusion
Digital transformations have significantly changed teaching and learning practices. The present study examines the new needs of employee development and empowerment in the digital age, identifying the components of artificial intelligence and digital curriculum. The main objective of the present study is to define the components of artificial intelligence and then apply them in the form of digital curriculum elements. In other words, the digital curriculum in the workplace is defined by the components and functions of artificial intelligence.This model is designed based on the phase logic of artificial intelligence and can help to improve the design of the digital workplace curriculum. Based on the background studies, no research was found that could organize the digital workplace curriculum in this way, and therefore, the findings of the current research and the final output were completely unique.
Mozhgan Oroji, Nadjla Hariri, Fahimeh Babalhavaeji,
Volume 11, Issue 3 (12-2024)
Volume 11, Issue 3 (12-2024)
Abstract
Introduction
Introduction There are many data collections in decision-making and every day a large number of these data are collected in research projects by humans or by devices and in this data, to better understand the issues related to data, we need to first understand the data and the literacy related to them. Data literacy is defined as information by reading, creating and communicating with data: that we can find data, make information about it, learn the tools to work with data, have less management of it. We can have, analyze and refine data, learn to share data and make simple decisions.
Research data management includes; production, access, tools, storage and reuse of research data with sufficient and easy-to-use help in virtual research infrastructures that form the main part of the monitoring cycle, which itself includes ideation. It is to create or receive, evaluate, select, ingest, preserve, store, access, reuse (Cox and Verban, 2014).
Studies on research data management are now common, while there is a global ease of research data, but it continues to be difficult to keep data easily accessible. Session, we know more than yesterday about the role of research data in the design and implementation of new research, but the trends and infrastructure to support researchers in research data management, need. (Varana, 2024).
Considering the research that has been conducted on research data management literacy, the aim of this study is to determine the components and indicators of management literacy. ) and to provide a suitable model for research data management literacy.
Methods and Materoal
The present study was conducted with a quantitative and survey method and aimed at evaluating and validating the tool built using the proposed research model. The statistical population of the National Institute of Higher Education Research and Planning was 112 academic centers affiliated with the Ministry of Science and the total number of faculty members of the humanities and social sciences of the country's public universities was 8,441. Due to the large volume of data, 360 people were selected using cluster sampling. Then, the questionnaire was completed and descriptive statistical methods (mean, deviation indices, frequency table) and inferential statistics (structural equation modeling and exploratory factor analysis) and SPSS and Smart Pls software were used to analyze the data.
Resultss and Discussion
The findings indicate that the six factors of stakeholders, services, policy, types of literacy, data cycle, and financial issues are critical together, explaining 60 percent of the total variance of changes. Also, the highest level of the level is related to the stakeholders factor with a mean of 4.09 and a standard deviation of 0.57, followed by the factors of services, policy, data life cycle, types of literacy, and financial issues, respectively. Using the Pearson correlation coefficient test, it was shown that all components of research data management literacy have a positive and significant correlation with the set at the 0.01 error level. The coefficients of the factor loadings of the subscales of research data management literacy also have a good understanding of the concept of their analysis and have a strong and significant correlation with their belief.
Conclusion
Research data management contributes to scientific integrity at different levels. When research data management literacy is sufficient, research data are accurate, complete, valid, and reliable. The risk of losing or damaging data, as well as the risk of unauthorized access, is minimized. In addition, research data can be shared with others with minimal effort and individuals can easily confirm the results.
The relationships between the components and indicators of research data management literacy from the perspective of faculty members in the humanities and social sciences of Iranian public universities show that higher than any of these components in improving the quality and efficiency of research, research data management literacy has a positive effect. The search for understanding the methods and infrastructures related to data management is a research for individuals to achieve better research results and valuable results. The results of this study show that different levels of research data management literacy among university professors know, and also need to have literacy skills in research data management that they do and create. Collecting, processing, validating, publishing, sharing, and archiving data are involved, and this is a characteristic of good research data management.
Introduction There are many data collections in decision-making and every day a large number of these data are collected in research projects by humans or by devices and in this data, to better understand the issues related to data, we need to first understand the data and the literacy related to them. Data literacy is defined as information by reading, creating and communicating with data: that we can find data, make information about it, learn the tools to work with data, have less management of it. We can have, analyze and refine data, learn to share data and make simple decisions.
Research data management includes; production, access, tools, storage and reuse of research data with sufficient and easy-to-use help in virtual research infrastructures that form the main part of the monitoring cycle, which itself includes ideation. It is to create or receive, evaluate, select, ingest, preserve, store, access, reuse (Cox and Verban, 2014).
Studies on research data management are now common, while there is a global ease of research data, but it continues to be difficult to keep data easily accessible. Session, we know more than yesterday about the role of research data in the design and implementation of new research, but the trends and infrastructure to support researchers in research data management, need. (Varana, 2024).
Considering the research that has been conducted on research data management literacy, the aim of this study is to determine the components and indicators of management literacy. ) and to provide a suitable model for research data management literacy.
Methods and Materoal
The present study was conducted with a quantitative and survey method and aimed at evaluating and validating the tool built using the proposed research model. The statistical population of the National Institute of Higher Education Research and Planning was 112 academic centers affiliated with the Ministry of Science and the total number of faculty members of the humanities and social sciences of the country's public universities was 8,441. Due to the large volume of data, 360 people were selected using cluster sampling. Then, the questionnaire was completed and descriptive statistical methods (mean, deviation indices, frequency table) and inferential statistics (structural equation modeling and exploratory factor analysis) and SPSS and Smart Pls software were used to analyze the data.
Resultss and Discussion
The findings indicate that the six factors of stakeholders, services, policy, types of literacy, data cycle, and financial issues are critical together, explaining 60 percent of the total variance of changes. Also, the highest level of the level is related to the stakeholders factor with a mean of 4.09 and a standard deviation of 0.57, followed by the factors of services, policy, data life cycle, types of literacy, and financial issues, respectively. Using the Pearson correlation coefficient test, it was shown that all components of research data management literacy have a positive and significant correlation with the set at the 0.01 error level. The coefficients of the factor loadings of the subscales of research data management literacy also have a good understanding of the concept of their analysis and have a strong and significant correlation with their belief.
Conclusion
Research data management contributes to scientific integrity at different levels. When research data management literacy is sufficient, research data are accurate, complete, valid, and reliable. The risk of losing or damaging data, as well as the risk of unauthorized access, is minimized. In addition, research data can be shared with others with minimal effort and individuals can easily confirm the results.
The relationships between the components and indicators of research data management literacy from the perspective of faculty members in the humanities and social sciences of Iranian public universities show that higher than any of these components in improving the quality and efficiency of research, research data management literacy has a positive effect. The search for understanding the methods and infrastructures related to data management is a research for individuals to achieve better research results and valuable results. The results of this study show that different levels of research data management literacy among university professors know, and also need to have literacy skills in research data management that they do and create. Collecting, processing, validating, publishing, sharing, and archiving data are involved, and this is a characteristic of good research data management.
Rahman Marefat, F Fatemeh Bazzi, Ghasem Azadi,
Volume 11, Issue 4 (1-2025)
Volume 11, Issue 4 (1-2025)
Abstract
Background and purpose: The user interface is the intermediary between the computer environment and humans, and paying attention to graphic elements and design criteria results in user satisfaction. This research has reviewed Persian articles published in the field of user experience in symbols and visual signs in website and application graphics.
Method: This research was conducted based on the prism method, with the aim of a systematic review. After the initial search, the number of 86 articles published in Persian language databases and publications as well as conferences in this field were obtained, and after screening, 61 articles were selected for study and analysis.
Findings: In general, 132 researchers worked in this field, of which 83 are men and 49 are women. The participation rate of men (63%) and women (37%) was measured. Three librarianship and information publications with the frequency of 9 articles, human and information interaction with 8 articles and national studies of librarianship and information organization with 7 articles were the most sources of article publishers in the field of user experience in icons and visual symbols in website and application graphics. Although it is customary to use all three quantitative, qualitative and combined approaches among researchers in the field of user experience in the symbols and visual signs in website and application graphics, but most of the researches conducted in this field have used survey methods and quantitative approach.
Conclusion: The thematic analysis of published sources in the subject area of user experience in symbols and visual signs in website and application graphics showed that the authors of this area have published works in this area in 26 thematic areas. Out of a total of 61 articles, the most published articles were 33 scientific research articles (54.1 percent) and the least type of conference articles were 10 articles (16.3 percent). Review and promotional articles included a total of 18 articles (29.6 percent) out of a total of 61 articles.
Sorush Fathi, , , ,
Volume 11, Issue 4 (1-2025)
Volume 11, Issue 4 (1-2025)
Abstract
Introduction:
The role of women, particularly female heads of household, has been a significant topic in social research, especially in the context of empowerment and economic independence. Women who become the primary earners in their households often face unique challenges related to gender expectations, financial responsibilities, and societal perceptions. The study of their lived experiences provides valuable insights into the factors that contribute to their empowerment or hinder their progress. This research seeks to explore the lived experience of economically empowered female heads of household, aiming to construct a locally relevant model or pattern that can be used to support other women in similar situations. By focusing on the personal, familial, and societal factors influencing these women's empowerment, the study contributes to a deeper understanding of the social dynamics at play and proposes strategies for promoting economic independence among female heads of household.
Methods and Materials:
A qualitative research design was employed for this study, using in-depth interviews as the primary data collection method. Snowball sampling was used to identify participants, ensuring a diverse group of economically empowered female heads of household. The interviews were conducted in person, with the researcher continuing to interview participants until theoretical saturation was achieved. A total of 13 women were interviewed for this study. The data were analyzed using open, axial, and selective coding techniques to identify the key themes and concepts related to their lived experiences. The codes were then categorized into broader themes, and a central phenomenon was identified.
Results and Discussion:
The analysis revealed a complex interplay of structural, contextual, and personal factors that influenced the economic empowerment of female heads of household. At the structural level, the dominance of gender culture, societal expectations, and the overwhelming commitments of women were identified as significant barriers. These women often had to navigate the intersection of traditional gender roles with the demands of being the primary financial provider for their families. In many cases, the conditions of employment were not aligned with the specific needs of female heads of household, leading to significant challenges in balancing work and family responsibilities. Contextual factors, such as the absence of a husband, the responsibility of caring for children, and the need to provide for the family’s basic needs, were also found to be crucial in shaping the experiences of these women. The lack of supportive resources and institutional frameworks further exacerbated these challenges. In particular, the absence of family support, financial resources, and access to training or employment opportunities created significant barriers to economic empowerment. Despite these challenges, several strategies emerged that helped these women overcome obstacles and achieve economic empowerment. At the individual level, continuing education, vocational training, and participation in professional development courses were found to be important. These strategies helped women acquire new skills, build confidence, and gain the qualifications needed to secure better employment opportunities. At the family level, the support of family members, including emotional and financial assistance, was identified as a critical factor in overcoming obstacles. Financial planning, budgeting, and resource management were also key strategies that contributed to the women's ability to maintain financial stability and improve their economic position. At the societal level, access to government and organizational support services played a crucial role in the empowerment process. Financial assistance, job placement services, and social protection programs were all found to be instrumental in helping these women gain financial independence. Furthermore, social recognition and the improvement of their social standing contributed to their sense of empowerment and self-worth. The central phenomenon identified in this study was the desire for economic empowerment. While these women did not initially possess the economic experience or readiness required for headship, they gradually developed the skills and knowledge needed to manage their families’ financial needs. Economic empowerment allowed these women to reduce their social exclusion, alleviate poverty, and integrate into social networks, improving both their personal and social conditions.
Conclusion
The findings of this study highlight the complex factors influencing the economic empowerment of female heads of household. By understanding the lived experiences of these women, it becomes clear that empowering them requires a multi-faceted approach that addresses structural, contextual, and personal challenges. Policymakers and social planners must consider the specific needs of female heads of household and provide the necessary support, resources, and opportunities to facilitate their empowerment. The results of this study underscore the importance of education, training, family support, and social services in helping these women overcome obstacles and achieve economic independence. Furthermore, recognizing the agency of women in this process is crucial for fostering social change and promoting gender equality.
The role of women, particularly female heads of household, has been a significant topic in social research, especially in the context of empowerment and economic independence. Women who become the primary earners in their households often face unique challenges related to gender expectations, financial responsibilities, and societal perceptions. The study of their lived experiences provides valuable insights into the factors that contribute to their empowerment or hinder their progress. This research seeks to explore the lived experience of economically empowered female heads of household, aiming to construct a locally relevant model or pattern that can be used to support other women in similar situations. By focusing on the personal, familial, and societal factors influencing these women's empowerment, the study contributes to a deeper understanding of the social dynamics at play and proposes strategies for promoting economic independence among female heads of household.
Methods and Materials:
A qualitative research design was employed for this study, using in-depth interviews as the primary data collection method. Snowball sampling was used to identify participants, ensuring a diverse group of economically empowered female heads of household. The interviews were conducted in person, with the researcher continuing to interview participants until theoretical saturation was achieved. A total of 13 women were interviewed for this study. The data were analyzed using open, axial, and selective coding techniques to identify the key themes and concepts related to their lived experiences. The codes were then categorized into broader themes, and a central phenomenon was identified.
Results and Discussion:
The analysis revealed a complex interplay of structural, contextual, and personal factors that influenced the economic empowerment of female heads of household. At the structural level, the dominance of gender culture, societal expectations, and the overwhelming commitments of women were identified as significant barriers. These women often had to navigate the intersection of traditional gender roles with the demands of being the primary financial provider for their families. In many cases, the conditions of employment were not aligned with the specific needs of female heads of household, leading to significant challenges in balancing work and family responsibilities. Contextual factors, such as the absence of a husband, the responsibility of caring for children, and the need to provide for the family’s basic needs, were also found to be crucial in shaping the experiences of these women. The lack of supportive resources and institutional frameworks further exacerbated these challenges. In particular, the absence of family support, financial resources, and access to training or employment opportunities created significant barriers to economic empowerment. Despite these challenges, several strategies emerged that helped these women overcome obstacles and achieve economic empowerment. At the individual level, continuing education, vocational training, and participation in professional development courses were found to be important. These strategies helped women acquire new skills, build confidence, and gain the qualifications needed to secure better employment opportunities. At the family level, the support of family members, including emotional and financial assistance, was identified as a critical factor in overcoming obstacles. Financial planning, budgeting, and resource management were also key strategies that contributed to the women's ability to maintain financial stability and improve their economic position. At the societal level, access to government and organizational support services played a crucial role in the empowerment process. Financial assistance, job placement services, and social protection programs were all found to be instrumental in helping these women gain financial independence. Furthermore, social recognition and the improvement of their social standing contributed to their sense of empowerment and self-worth. The central phenomenon identified in this study was the desire for economic empowerment. While these women did not initially possess the economic experience or readiness required for headship, they gradually developed the skills and knowledge needed to manage their families’ financial needs. Economic empowerment allowed these women to reduce their social exclusion, alleviate poverty, and integrate into social networks, improving both their personal and social conditions.
Conclusion
The findings of this study highlight the complex factors influencing the economic empowerment of female heads of household. By understanding the lived experiences of these women, it becomes clear that empowering them requires a multi-faceted approach that addresses structural, contextual, and personal challenges. Policymakers and social planners must consider the specific needs of female heads of household and provide the necessary support, resources, and opportunities to facilitate their empowerment. The results of this study underscore the importance of education, training, family support, and social services in helping these women overcome obstacles and achieve economic independence. Furthermore, recognizing the agency of women in this process is crucial for fostering social change and promoting gender equality.
Ms Elham Askarian Kakh, Ms Somaye Sadat Akhshik, Mr Abdolhossein Farajpahlou, Mr Reza Rajabali Beglou,
Volume 11, Issue 4 (1-2025)
Volume 11, Issue 4 (1-2025)
Abstract
Purpose: The goal of this study was to analyze the situation of information poverty among primary healthcare workers in Tehran. The main research question was to investigate their information poverty based on Chatman's information poverty theory. According to Chatman's theory, people who do not believe that information from outside the group can help them, tend to perform self-protective behaviors that limit access to information. Such behaviors intensify the information poverty of the primary healthcare workers, and since they are involved in the transfer of information between specialists and non-specialists, their information poverty can affect the society's information poverty. Therefore, it is necessary to assess the information poverty of primary healthcare workers in the field of health.
Method: This survey study was conducted by identifying the information poverty indicators and measuring the information poverty of 154 primary healthcare workers. Sampling was done by stratified random method, and data collection was done through a researcher-made questionnaire.
Results: The "deception" component indicated an attempt to present a false reality. "Secrecy" means non-disclosure of information, in the second place; And after that, there was "Situational Relevance" which indicated the selection of information. The lowest average was related to "Risk-taking", which showed the fear of the primary healthcare workers about the consequences of searching information. 41.6% of the primary healthcare workers were in the deceptive group, 21.6% in the situational relevance group, 19.2% in the secretive group and 13.6% in the risk-taking group.
Conclusion: The results showed the information poverty of primary healthcare workers. Most of them had a tendency to deceive others. A fifth of people paid attention to the situational relevance of the information. Some have fueled their information poverty by secrecy, and fewer by risk aversion. The placement of some in two categories refers to the overlap of some components. The most effective factor of deception was "reducing the risk of information seeking" and "distrust". "Value of information" and "use of insider information" were influential in situational relevance. "Coping with a lack of information processing skills" and "mistrust" were the main motivations behind the secrecy. "Regarding information as irrelevant" and "preservation of privacy" were effective on risk aversion.
Samaneh Shadmanfar, Fatemeh Fahimnia, Abdolreza Noroozi Chakoli, Javad Taghizadeh Naeeni,
Volume 11, Issue 4 (1-2025)
Volume 11, Issue 4 (1-2025)
Abstract
Introduction: The main purpose of this research is to develop a conceptual framework for personal reputation management in social networks. This study is conducted from the perspective of information science and focuses on prior research in the field of creating and managing reputation through citation behaviors, especially in bibliometrics. In this research, the main question is how social network users manage their personal reputation by using online information. Ultimately, it will be clarified to what extent social media users replicate researchers’ citation behaviors in creating and managing academic reputation.
Methods: In this study, a multi-stage method was employed for data collection. Data were collected through daily journal entries and semi-structured interviews with 30 professional and managerial social media users. The findings of the study were extracted and analyzed using thematic analysis, resulting in six main themes and nineteen sub-themes categorized under two parts: identity creation and reputation management.
Results: The findings show that the representation of different online personalities aids in providing (rather than creating) users’ online identity. Information-sharing behaviors for creating and managing reputation vary according to the social media platform. Additionally, managing online communications and censorship is crucial for reputation protection. Maintaining professional reputation is more important for users than private reputation. Users are aware of the “border management” between their professional and private lives in the online context and understand its impact on personal reputation management.
Conclusion: This research reveals the relationship between citation behaviors and informational behaviors in social networks regarding personal reputation. In general, the research findings indicate that social network users tend to replicate researchers’ citation behaviors in online environments, even if this occurs unintentionally. The results of this study provide useful insights into the role of online information in personal reputation management and will help develop theories related to informational behaviors
Kimiya Taghizadeh Milani, Massomeh Karbala Aghaei Kamran, Amir Ghaebi,
Volume 11, Issue 4 (1-2025)
Volume 11, Issue 4 (1-2025)
Abstract
Purpose: The main purpose of the current research is to identify the components of evaluation and selection of research data for the use of data repositories operators based on international texts.
Method: This research was conducted using the meta-synthesis method and the Sandelowski and Barroso (2007) framework. Relevant keywords were utilized to search for studies in databases of various publishers and through citation tracking to ensure comprehensive coverage of identified sources. After multi-stage screening and applying inclusion and exclusion criteria, 48 English-language sources were selected from an initial pool of 3,080 sources. Additionally, the Kappa coefficient was employed to ensure the quality of extracted codes and maintain the reliability of the designed model.
Findings: The criteria for evaluating and selecting research data for preservation in data repositories were categorized into eight components, including Data preparation, data quality, physical conditions and technical features, metadata management and characteristics, ethical principles of data, document-related criteria, alignment with FAIR principles, and repository policies and issues, encompassing a total of 52 indicators.
Conclusion: This study identified criteria for evaluating and selecting research data for preservation in data repositories based on global authoritative literature. Applying these criteria can lead to sustainable preservation of data in repositories, enhance data quality, and strengthen research infrastructure.
Method: This research was conducted using the meta-synthesis method and the Sandelowski and Barroso (2007) framework. Relevant keywords were utilized to search for studies in databases of various publishers and through citation tracking to ensure comprehensive coverage of identified sources. After multi-stage screening and applying inclusion and exclusion criteria, 48 English-language sources were selected from an initial pool of 3,080 sources. Additionally, the Kappa coefficient was employed to ensure the quality of extracted codes and maintain the reliability of the designed model.
Findings: The criteria for evaluating and selecting research data for preservation in data repositories were categorized into eight components, including Data preparation, data quality, physical conditions and technical features, metadata management and characteristics, ethical principles of data, document-related criteria, alignment with FAIR principles, and repository policies and issues, encompassing a total of 52 indicators.
Conclusion: This study identified criteria for evaluating and selecting research data for preservation in data repositories based on global authoritative literature. Applying these criteria can lead to sustainable preservation of data in repositories, enhance data quality, and strengthen research infrastructure.
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